Marketing Mix Modeling & Multi-Touch Attribution – Working towards unified measurement
With marketers facing an increasingly complicated media environment, traditional marketing techniques such as Marketing Mix Modeling (MMM) or Multi-Touch Attribution (MTA) assess the value of marketing channels in varying levels of detail. By combining the benefits of both tools, unified measurement, which considers marketing value across all channels, can provide a more rounded view of how to optimize spend and increase revenue.
This blog will show how MTA and MMM can be used together to achieve unified measurement of marketing performance.
Why is MTA important?
Previously confined to less sophisticated methods such as first-click or last-click attribution, multi-touch attribution (MTA) allows marketers to fully understand their consumers’ digital journey. MTA’s advanced data-driven attribution (DDA) algorithms provide insights on a granular level, measuring all online or offline touchpoints that played a role in the route to conversion.
Consumer data provided at this level helps marketers to assess how much credit each online and offline marketing activity deserves, where previous models overvalue certain channels based on their place in the customer journey.
Fospha’s MTA product embraces the complexity of customer journeys, helping clients to consolidate fragmented data sources. This data is then organized in their customer data platform (CDP), where advanced algorithms attribute credit to different touchpoints, showing the true value of all channels that lead to conversion.
Whilst MTA’s high level of detail provides key insights on a channel by channel basis, this degree of granularity doesn’t consider broader influences that might impact marketing performance. This means that marketers might act on accurate insights and make the best decision possible, but still fail to meet sales targets, due to external factors such as inflation, or the weather.
What is MMM and what does it deliver for marketers?
While MTA is a powerful observational tool, MMM provides strategic insight into the broader interactions between marketing channels and external factors, allowing marketers to carry out diagnostic analysis on past campaigns and predict the success of future ones. It’s effectively a data-based planning tool.
Providing top-down measurement of marketing effectiveness, MMM shows how channels work together towards a common marketing objective, in the context of external influences such as public holidays or political climate. Measurement of past campaign performance, in combination with information on external influences (created by aggregating historical data from sources such as radio, print, or TV), can then be used to predict the success or failure of future campaigns, based on seasonality.
A greater understanding of external influences also helps to assess the performance of online or offline channels which have little or no visibility. Greater visibility of the interaction between marketing streams is especially important for lower-funnel channels which, although they rarely result in direct sales, often play an important role in the conversion process.
With many companies battling against data fragmentation across their business, standardizing the data-gathering process through a singular data platform helps to break down these data silos. This unified view of marketing performance leaves marketers with more time to implement important insights, instead of hunting for missing data, or cleaning corrupted files.
Fospha’s MMM product is powered by their data platform, which can integrate cost and revenue data, external data feeds, and existing customer data. Fospha created a customer data platform for the client which aggregates the data, providing visualizations alongside a predictive planning tool, making it easy for clients to integrate data-driven decision making into their strategy and planning process.
The diagnostic and predictive capabilities of the product highlight the most successful marketing channels with more accuracy and predict the outcome of future campaigns. The ability to run simulations based on different marketing mixes, external influences, or spend, means that marketers can move quickly from insight to action – it also aids in managing budgets and expectations, predicting in real-time how a set amount of spend translates to a certain number of leads.
So how do MMM and MTA work towards unified measurement?
Both measurement methods look at data in different levels of detail, meaning that in order to have a more comprehensive overview, marketers need to have both.
Whilst MTA gives a clear view of the success or failure of different channels, MMM can help marketers to understand why a strategy has failed or succeeded. MMM can then predict what will happen if existing spend is redistributed across different marketing channels.
The ability to carry out simulations, using Fospha’s highly agile MMM product, helps marketers to apply their learning from MTA, adjusting variables in order to optimize return on investment, or reduce cost per acquisition. This is a departure from human, benchmark-driven approaches to marketing strategy, replacing gut instinct with data-driven, omnichannel decisions.
Unified measurement is a logical evolution from specific measurement capabilities such as MMM or MTA – it’s through combining these methodologies that companies can achieve a more holistic view of campaign performance and increase marketing effectiveness.